Metagenomic Characterization of Poultry Cloacal and Oropharyngeal Swabs in Kenya Reveals Bacterial Pathogens and Their Antimicrobial Resistance Genes

Poultry enteric bacterial diseases are of significant economic importance because they are responsible for production losses due to weight loss, increased morbidity and mortality, and increased cost of production arising from poor feed conversion and treatment. This cross-sectional purposive study characterized enteric bacterial pathogens in poultry from selected agroclimatic regions in Kenya and investigated their antimicrobial resistance gene profiles. Cloacal (n = 563) and oropharyngeal (n = 394) swabs were collected and pooled into 16 and 14 samples, respectively, to characterize bacterial pathogens and their antimicrobial resistance gene profiles. We report that Proteobacteria, Chlamydiae, and Firmicutes are the most dominant phyla present in both cloacal and oropharyngeal swabs of the six poultry species studied, indicating the colonization of the poultry gut by many pathogenic bacteria. Using KEGG and COG databases, some pathways related to metabolism, genetic information, and cellular processing were detected. We also report the abundance of antimicrobial resistance genes that confer resistance to β-lactamases, aminoglycosides, and tetracycline in most of the poultry analyzed, raising concern about the dangers associated with continuous and inappropriate use of these antibiotics in poultry production. The antimicrobial resistance gene data generated in this study provides a valuable indicator of the use of antimicrobials in poultry in Kenya. The information generated is essential for managing bacterial diseases, especially in backyard poultry raised under scavenging conditions.


Introduction
Poultry farming is practiced in many parts of the world because of its economic importance.Poultry meat and egg production is a source of livelihood for farmers and a major protein source for consumers.Te major reasons for the popularity of poultry farming are the minimal religious and cultural restraints on their consumption, in addition to their relatively low costs of production [1].Currently, poultry meat is the most widely consumed meat type, accounting for 35% of the meat consumed globally [2].
Te most common poultry raised in Kenya include chickens, ducks, guinea fowls, quails, geese, turkeys, pigeons, and ostriches.Tree methods are used for rearing poultry: the free-range system, the semi-intensive system, and the intensive system.In Kenya and other developing countries, poultry is mainly raised under free-range (scavenging) systems in rural settings; hence, it is commonly referred to as village poultry [3].Te free-range or backyard system is popular in rural areas because it is less capital-intensive and applies little to no biosecurity measures [4].In addition, indigenous African poultry are known to be more tolerant to diseases and harsh environmental conditions than commercial chickens, comprising heterogeneous populations [5].
Te demand for poultry products has pushed many farmers to intensify poultry production over the last century, resulting in rapid growth in the industry.Te current poultry biomass, for instance, accounts for approximately 70% of the total biomass of birds worldwide [6,7].Poultry focks are often kept in high-density populations that are genetically homogeneous.Tis potentially makes them susceptible to outbreaks of infectious diseases, leading to signifcant economic losses and food insecurity [7].Te extensive utilization of the same antimicrobial classes in humans as well as veterinary medicine (such as treatment and growth promotion in poultry) is also contributing to antimicrobial resistance (AMR) selection, which is a major public health concern [8].Tere is therefore a need to characterize bacterial pathogens in backyard poultry and also profle the antimicrobial resistance genes (ARGs) in these poultry species.
Several approaches have been employed for studying poultry gut microbiota, with the earliest being the culturebased methods [9,10].Unfortunately, these methods were prone to bias and inaccuracy as most microorganisms were not cultivatable because of unknown growth requirements [1].Several polymerase chain reaction (PCR)-based techniques have also been exploited for evaluating microbial profles and detecting antimicrobial resistance genes in poultry, such as the Sanger sequencing technology.Although these methods improved the sensitivity and speed of detection of microorganisms and their antimicrobial resistance genes, they were still unable to represent the gut microbiota accurately due to their low coverage [11].Additionally, they were time-consuming, costly, and insufcient in refecting the true diversity of the gut microbiota [11].
Sequence-based metagenomics, involving the extraction, fragmentation, size-separation, and random direct sequencing of DNA from an environmental sample, has become the method of choice for studying microbial communities due to its high accuracy [1].Previously, the more commonly used sequencing technique involved amplifcation and sequencing of either the 16S rRNA gene (for bacteria and archaea) or the internal transcribed spacer (ITS) region (for fungi) in the sample DNA [1].However, direct shotgun sequencing of the DNA sample of the entire microbial community has become more popular due to its high sensitivity, reproducibility, and coverage.Metagenomic analysis is thus a powerful tool for studying microbial communities and their importance in various environments, including the gastrointestinal tracts of animals.Tis approach allows for the identifcation of both cultivable and noncultivable microorganisms and their associated genes, thus providing a more comprehensive picture of the microbial ecology of poultry [1].
Unfortunately, only a few studies have applied metagenomics to investigate bacterial communities and antimicrobial resistance genes (ARGs) present in poultry raised in free-range environments in Kenya.For instance, a study by Nduku et al. [12] found a high prevalence of extendedspectrum beta-lactamase (ESBL)-producing Escherichia coli in poultry in Kenya.However, several studies have investigated the prevalence of bacterial pathogens and ARGs in poultry elsewhere.For instance, Havelaar et al. [13] estimated that the global burden of food-borne illness due to nontyphoidal Salmonella in poultry was over 60 million cases yearly.In addition, a study in Poland comparing the AMR gene profles of farm animals exposed to antimicrobial treatment to those of wild animals that seemed not to be subjected to antimicrobial pressure revealed higher levels of AMR in farm animals than in wildlife [8].Furthermore, Skarżyńska et al. [8] underscored the potential of wildlife in disseminating AMR.In another study in China, microbial community and resistome profles in cecal, cloacal, and fecal samples of broilers were compared to determine the feasibility and comparative merits and demerits of using particular sample types to study gut microbiota [14].Te authors observed that fecal microbiota have limited potential as a proxy in chicken gut microbial community studies.Feces should therefore be used with caution when characterizing gut microbiomes.
Most metagenomic studies on poultry microbiomes have been carried out on poultry raised under controlled and regulated feeding regimes.However, metagenomic studies on free-ranging poultry are more informative than those on poultry raised under controlled conditions [15].Tis is because free-ranging poultry are exposed to a broader range of environmental conditions, which can infuence their microbial communities [16].In contrast, poultry raised under controlled conditions are exposed to a more homogeneous environment, which may limit their bacterial communities' diversity and afect their ARG profles [17].To our knowledge, only one study in Ethiopia investigated the microbial community profles of indigenous backyard chickens on a scavenging feeding system from two geographically and climatically distinct regions [18].Metagenomics analysis of poultry raised under a free-range feeding system is therefore required to explore the impact of local feed (plants, insects, and other small animals) on poultry health [18].In addition, this aids in understanding the microbiome compositional structure of the environment in free-ranging poultry.In this study, we characterized bacterial pathogens and ARGs present in the cloacal and oropharyngeal regions of free-range poultry in Kenya using a metagenomic approach.Te cloacal and oropharyngeal swab samples have been widely used for the detection of poultry pathogens because most bacterial and viral infections in birds are mainly through the fecal-oral route, making these regions a critical study area [19,20].Our fndings will contribute to a better understanding of the gut bacterial pathogens of poultry raised in free-range environments and inform us of the interventions needed to reduce the risk of food-borne illnesses and antimicrobial resistance.

Sample Collection.
Tis study was carried out from 2016 to December 2018 across six counties with varying agroecological conditions in Kenya (Figure 1).Te study received institutional clearance from the Jomo Kenyatta University of Agriculture and Technology (JKUAT) to conduct animal research.Clearance was also sought from the Director of Veterinary Services from the State Department of Livestock, Ministry of Agriculture, Livestock Development, and Co-operatives, Kenya, to study farm animals.A stratifed cross-sectional purposive approach was used during sample collection.Te study areas were divided into subcounty populations to reduce sample bias.Te maximum possible number of households per subcounty population was then considered.Households were selected based on their willingness to participate in the study.A distance of 0.5 km between households was maintained to avoid chances of sampling related individuals.
Te study collected cloacal (n � 563) and oropharyngeal (n � 394) swabs from selected regions in Kenya with distinct geographic and climatic conditions.Te targeted regions included counties bordering Uganda (Bungoma, Busia, and Trans Nzoia), maritime borders (Kilif and Kwale), and urban areas of Nairobi (Figure 1).In addition, information on fock condition or performance was also collected.Te collected cloacal and oropharyngeal swab samples were immediately frozen in dry ice and later placed in liquid nitrogen in the feld.Tey were then processed in preparation for downstream analysis or permanently preserved at −80 °C until processing.

Extraction of Nucleic Acids and Sequencing.
Te cloacal and oropharyngeal swab samples were processed in pools (16 pools representing the 563 cloacal swabs and 14 pools representing the 394 oropharyngeal swabs) (Supplementary Materials Tables S1 and S2).

DNA Extraction.
DNA was extracted from the pooled cloacal and oropharyngeal swabs using the PureLink Genomic DNA Mini Kit (Invitrogen, Termo Fisher Scientifc, Waltham, Massachusetts, USA) following the manufacturer's protocol.Briefy, the swab was placed into a 2 ml Eppendorf tube to which 200 μl of phosphate-bufered saline (PBS) and 20 μl of proteinase K were added and mixed well by pipetting.An equal volume (200 μl) of PureLink R Genomic Lysis/Binding Bufer was added to the lysate and mixed well by vortexing briefy before incubating at 55 °C for at least 10 minutes.Te lysate was briefy centrifuged at 3,000 × g and 200 μl of 99% ethanol was added and mixed well by vortexing for 5 seconds.Te lysate was then added to a PureLink R Spin Column attached to a collection tube and centrifuged at 10,000 × g for 1 minute at room temperature.Te collection tube was discarded, and the spin column was placed into a clean PureLink R collection tube.To wash the extracted DNA, 500 μl of the wash bufer 1 prepared with ethanol was added to the column and centrifuged at room temperature at 10,000 × g for 1 minute.Te collection tube was discarded, and the spin column was placed into a clean PureLink R collection tube.A second washing was done by adding 500 μl of wash bufer 2 to the column and centrifuged at maximum speed for 3 minutes at room temperature, and the collection tube was discarded.Te spin column was fnally placed in a sterile 1.5 ml microcentrifuge tube, and 50 μl of PureLink R Genomic Elution Bufer was added to the column, which was incubated at room temperature for 1 minute and centrifuged at maximum speed for 1 minute at room temperature.To recover more DNA, a second elution step using the same elution bufer volume as the frst was performed in another sterile, 1.5 ml microcentrifuge tube.Te column was then removed and discarded.Te purifed DNA solution was stored at a −20 °C freezer until it was processed at the International Livestock Research Institute (ILRI) genomic platform where library preparation and whole genome shotgun sequencing were done.

2.2.2.
Sequencing.Te quality and quantity of the DNA preparations were determined in the NanoDrop ™ 2000 spectrophotometer and Qubit fuorometer (Invitrogen, Termo Fisher Scientifc, Inc., Waltham, Massachusetts, USA), respectively.Te extracted genomic DNA was used to prepare indexed paired-end libraries using Nextera ™ XT DNA Library Preparation Kit according to the manufacturer's instructions (Illumina, Inc., USA).Indexed samples were pooled and reconstituted to 4 nM before diluting to 12 pM for loading into the MiSeq instrument (Illumina, CA, USA) version 2 reagent kit (300 cycles) with a paired-end format (2 × 150 cycles) at the ILRI Genomic platform, Nairobi, Kenya.Te number of reads obtained from each library is shown in Tables 1 and 2.

Taxonomic Assignment.
Te metagenomic analysis was done using the Metaphlan version 3.0 [21] and SqueezeMeta version 1.5.1 workfows [22].Poor-quality sequencing reads (short contigs <200 bp) and adaptors were trimmed using Prinseq version 0.39 [23].Read mapping against host references was performed to remove host DNA using Bowtie2 version 2.4.5 [24].Te paired-end sequence reads were de novo assembled into contigs using Megahit version 1.0.2[25].Te assembled contigs were used for taxonomic assignment and functional annotation analyses.Taxonomical abundance was determined by comparing metagenomic reads to a database of taxonomically informative gene families to annotate each metagenomic homolog.Merged abundance tables used for the assignment of diferent taxonomic units were generated using Metaphlan version 3.0 [21].Sequences were therefore classifed using the RDP classifer into operational taxonomic units [26,27].An operational taxonomic unit (OTU)-based method was used for analysis where sequences were split into bins based on taxonomy [28][29][30].
Te merged abundance tables were used to assign taxonomies at diferent levels.We then used the SqueezeMeta version 1.5.1 workfow to generate the contigs that were used to create the phyloseq object using the phyloseq package in R version 4.3.0[31] and, consequently, the OTU table that was used for downstream analyses using the same workfow.Te analysis includes plotting rarefaction curves, alpha diversity indices (for analyzing microbial community diversity and richness), and beta diversity indices (for comparison of microbial diversity between diferent poultry species and sample types).PCoA analysis was also performed for taxonomic assignment to determine the distances between levels of classifcation.Phyloseq v1.44.0 and ggplot2 v3.4.2 packages in R were used to visualize the abundance of bacterial taxonomic composition.

Functional Annotation.
Te function of the coding sequence was inferred based on similarity to sequences in the Kyoto Encyclopedia of Genes and Genomes (KEGG) as proposed by Kanehisa and Goto [32] and Clusters of Orthologous Genes (COG) databases using diamond  [33] with a cutof of above 40% of the reference and query ratio being used.Clustering, principal component analysis (PCA), and nonmetric multidimensional scaling (NMDS) analyses were performed using the generated taxonomic and functional abundance tables.

Characterization of Antimicrobial Resistance Genes (ARGs).
Antimicrobial resistance genes (ARGs) from the poultry cloacal and oropharyngeal swab content were characterized to explore the relationship between diverse sequences and resistance levels.Te assembled contigs of cloacal and oropharyngeal swabs of the diferent poultry species were aligned against the NCBI AMRFinderPlus [34] and Resfnder [35] databases for mass screening of the assembled contigs for ARGs using ABRicate software version 1.0.1 [36].
Based on raw read counts, the relative abundances of AMR genes were estimated.Analysis and visualization of results on graphs and heat maps were carried out in the open source RStudio 3.5.3version for Windows (https://www.rproject.org/)using the library(vegan), library(ggplot2), library(reshape2), and library(RColorBrewer) packages.Te ARGs' relative abundance between the cloacal and oropharyngeal swabs and their distribution through hierarchical clustering in all classifcation levels are reported.

General Overview of the Sequence Data.
A total of 17,002,195 paired-end reads (from cloacal swab samples) and 11,050,372 paired-end reads (from oral-pharyngeal swab samples), with a median length of 200 base pairs (bp), were obtained from all samples (Tables 1 and 2).Te total number of clean reads generated from cloacal and oropharyngeal samples was 16,432,416 and 10,879,784, respectively.Tese were subsequently assembled into a total of 66,090 and 60,098 contigs, respectively.Using a 95% similarity cut-of, the assembled contigs yielded 301 and 275 operational taxonomic units (OTUs) for cloacal and oropharyngeal swabs, respectively.Tree samples (CN3, CN10, and DK3) were not informative as they did not generate any OTUs that could be used for taxonomic assignment.Rarefaction (discovery) curves generated from the OTUs show that all the samples approached a plateau, which suggests that the sample volumes were efcient in estimating both cloacal and oropharyngeal taxa (Figure 2).Analysis of species richness (observed number of OTUs and ACE) and community diversity (Chao1, Shannon, and Inverse Simpson indices) showed that there was no significant diference in species richness and diversity in cloacal and oropharyngeal samples across the poultry species except for the pigeons, which had much lower richness and diversity compared to other species (Tables 3 and 4).Tis implies that the species richness and diversity of the bacterial pathogens that colonize both the cloacal and oropharyngeal regions are generally similar across the diferent poultry species.
Te number of OTUs and Shannon entropy groupings of the diferent species by sample type and other alpha diversity measures by sample type and species are shown in Figure 3. Te results similarly showed that there was no marked diference in the species richness of the detected bacterial pathogens between the cloacal and oropharyngeal samples in the diferent poultry species.
A Wilcoxon rank-sum (Mann-Whitney) nonparametric test was used to determine whether the observed number of OTUs difered signifcantly between sample types.Te pairwise comparisons using the Wilcoxon rank sum test with continuity correction are provided (Supplementary Materials Tables S3-S5).Te results show no statistically signifcant diferences in the diversity of microbial communities between the cloacal and oropharyngeal samples for any of the diversity indices examined.
Diversity indices were also tested to determine whether they difered signifcantly between species (Supplementary Materials Tables S6-S8).Te results show the pairwise comparisons of species richness (observed, Shannon, and Chao1) between poultry species (chicken, duck, goose, guinea fowl, pigeon, and turkey).Based on observed richness, no signifcant diference was observed in the bacterial pathogen community richness between most of the poultry species (p > 0.05).However, in Shannon's diversity index, there is a signifcant diference in the bacterial species richness of pigeons compared to other species-a value of p < 0.05 was obtained for pigeons compared to ducks and geese.Using the Chao1 diversity index, a signifcant difference was observed in the richness of pigeons compared to chickens, ducks, and geese (p < 0.05).Tese results therefore suggest that pigeons have a diferent bacterial species richness when compared to other poultry species which do not difer signifcantly in species richness.

Cloacal and Oropharyngeal Bacterial Pathogen Composition across Poultry Species.
Phylum and genus-level distributions for individual samples are shown in Figure 4.At the phylum level, Proteobacteria, Chlamydiae, and Firmicutes were the most dominant phyla detected in cloacal and oropharyngeal samples across the poultry species.Chlamydiae were mostly detected in chicken samples, except for one pooled sample in ducks.Proteobacteria, on the other hand, was detected in chickens, ducks, and geese, while Firmicutes was detected in ducks and geese.Other phyla that were detected in cloacal samples included Bacteroidetes (in ducks and geese) and Tenericutes (in geese and chickens).Other phyla detected in oropharyngeal samples included Tenericutes (in chickens), Bacteroidetes (in ducks), and Actinobacteria (in geese).
Desulfovibrio, Gallibacterium, and Mycoplasma were the most dominant genera across the poultry species in the cloacal swabs.Other genera detected in some poultry species included Escherichia, Klebsiella, Chlamydia, Bacteroides, Enterococcus, and Avibacterium.Most of these bacteria are potentially pathogenic.In the oropharyngeal samples, the most dominant genera were Chlamydia, Escherichia, Avibacterium, Gallibacterium, Mycoplasma, Klebsiella, and International Journal of Microbiology Neisseria, which are common etiological agents of poultry diseases.
To assess the relatedness and overall taxonomic similarities between the identifed sequences in the cloacal and oral-pharyngeal swab samples, a hierarchical clustering analysis of the dominant genera and species of all samples for both groups was performed (Figure 5).Te hierarchical cluster maps for both groups generally had dendrograms with intermingled branches, implying a lack of clear separation between samples from the diferent poultry species.Te results therefore indicate the absence of bacterial pathogen-host specifcity for most of the samples studied.However, certain bacteria were only detected in the cloaca and not the oropharynx, and vice versa.Te hierarchical cluster maps also showed the dominance of Desulfovibrio, Gallibacterium, and Mycoplasma in the poultry cloacal swab samples.Additionally, the cluster map also showed that

International Journal of Microbiology
Chlamydia, Gallibacterium, Avibacterium, and Mycoplasma were the most dominant genera in the oropharyngeal swab samples across the poultry species.Species abundance was also resolved, revealing that Escherichia coli and Chlamydia ibidis were the most dominant bacterial species across the poultry species in cloacal samples, while Streptococcus suis, Chlamydia ibidis, Gallibacterium anatis, Avibacterium paragallinarum, Mycoplasma gallinaceum, and Weissella confusa dominated the poultry oropharynx.
Beta diversity analysis was performed to investigate the diversity between sample types (cloacal and oropharyngeal samples) and also between poultry species (chickens, ducks, guinea fowls, geese, pigeons, and turkeys).Te NMDS with Jaccard distance were used for dimension reduction analysis (Figure 6(a)).Te NMDS plots show that there is no difference between the two groups.Te principal coordinate analysis (PCoA) plot comparing the poultry microbiomes of cloacal and oropharyngeal samples by keeping parameter     Based on the ordination of the distance matrix generated using the Bray-Curtis complementary algorithm, a clear demarcation between bacterial assemblages from the cloaca and oropharynx was equally not apparent along the principal coordinate axis 1 (PC1) of the PCoA plot as the microbiota communities of the cloaca and oropharynx overlapped, indicating that the community structures of the two segments were similar across the poultry species (Figure 7).
Te separation was confrmed using the permutational analysis of variance (PERMANOVA), which tests whether the sample types difer signifcantly (Table 5).Te ANOVA test also suggests that the diference in diversity between the two groups is not statistically signifcant (p > 0.05).
Te PERMANOVA analysis also tested whether the poultry species difer signifcantly from each other (Table 6).Te results showed that the diference between metagenomes across the poultry species is signifcant (p < 0.05) with approximately 38% of the variations being determined by the poultry species type.Te ANOVA analysis also shows that the residual variation is relatively low, indicating that the variation that is not based on the species is small.

Functional Annotation.
Te functional diversity of a microbial community can be quantifed by annotating metagenomic sequences with functions [15].Classifcation of assembled metagenomic protein sequences into a protein family (function) requires searching protein family databases.We mapped protein-coding sequences against the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Cluster of Orthologous Genes (COG) databases.Relative abundance in level 1 hits of each database was plotted as a heatmap of functional abundance for each sample ( Te KEGG pathway analysis showed that genetic information processing, environmental information processing, and cellular processes were abundant in both cloacal and oropharyngeal samples.Te COG pathway analysis, on the other hand, showed that human diseases and metabolism were abundant in both cloacal and oropharyngeal samples, with functions such as cellular processes, signal transduction, environmental information processing, and information storage and processing being detected only in certain poultry species and sample types.
Just like in the cloacal samples, the major ARGs found in oropharyngeal samples also confer resistance to β-lactamases (TEM16, TEM33, and TEM4), aminoglycosides (aadA12, aadA, and aadA15), and tetracycline (tetC and  AAC6Ib7) (Figure 10).Guinea fowls, pigeons, and geese had higher concentrations of ARGs compared to the other poultry species.Cloacal samples generally had a higher number of ARGs compared to oropharyngeal swab samples.

Discussion
Several studies have underscored the considerable impact of gut microbiomes on poultry health and performance [37,38].It is therefore important to evaluate the community profles of important microorganisms colonizing the GIT of livestock, especially bacterial pathogens serving as etiological agents of livestock diseases.Metagenomic studies utilizing shotgun sequencing technologies have been used widely in recent years to study microbial populations since most microbes are not cultivable [18,39,40].Additionally, the 16S rRNA gene has also been extensively used as an important phylogenetic marker for studying microbial communities [11,39].Te main advantage that whole genome sequencing has over 16S rRNA and other marker-based sequencing methods is that it spans the entire genome of the microbes.Te generated sequence can therefore be aligned against ARGs and reference genomes in diferent databases to identify microbes even at strain level [18] as well as genes associated with AMR.However, despite using 16S rRNA or WGS in various animals like humans, pigs, and chickens, few studies have taken advantage of metagenomic analysis to investigate bacterial pathogens afecting backyard poultry managed in unregulated or scavenging systems.
Rarefaction (discovery) curve analysis of samples shows that all the samples approached a plateau, which suggests that the sample volumes were efcient in estimating both cloacal and oropharyngeal taxa, as alluded to by Andreani et al. [41].Pairwise comparisons of species richness (observed, ACE, Shannon, and Chao1) between diferent poultry species (chicken, duck, goose, guinea fowl, pigeon, and turkey) were tested to determine whether they difered signifcantly between species.Overall, the results suggest that pigeons have a distinctly lower species richness than other poultry species, which do not difer signifcantly.In a study conducted in India to investigate the molecular basis of diferential host responses to avian infuenza viruses in birds   14 International Journal of Microbiology with difering susceptibility, it was observed that pigeons showed the lowest number of diferentially expressed genes (DEGs) in most tissues, indicating a response to infection despite the low viral loads [42].Previous studies have also shown that pigeons were highly resistant to H5N1 infections, suggesting that they have an inherent ability to prevent viruses and other pathogens from entering cells or spreading [42,43], hence their low bacterial pathogen species richness and diversity.Tere were also no marked diferences in species richness between cloacal and oropharyngeal samples for the diferent species under study.Tis is similar to a study by Andreani et al. [41] comparing cloacal and cecal microbiome in broiler chickens from Northern Ireland which showed that cloacal and cecal microbiomes from the same individual were more similar than expected by chance.Unfortunately, it was not possible to compare our fndings on bacterial pathogen species richness and diversity to other published works on cloacal and oropharyngeal microbiomes because of insufcient literature comparing microbiomes in these two regions.We, therefore, recommend more studies to compare microbial community profles in the cloacal and oropharyngeal regions of poultry to help understand the similarities and diferences in the microbial composition and diversity in these two regions.
Our study reports that Proteobacteria, Chlamydiae, and Firmicutes were the most dominant phyla in the cloacal and oropharyngeal samples across the poultry species, which is consistent with the fndings by Kang et al. [14] that reported Firmicutes, Bacteroidetes, and Proteobacteria as the dominant phyla in the poultry in the hindgut and feces, although Bacteroidetes were detected in lower numbers in the current study.However, our results difer from previous observations by Yan et al. [40] and Kumar et al. [18], who suggested that Bacteroidetes and Firmicutes were the most abundant phyla in chickens.Tey also difer from the fndings by Andreani et al. [41] who found Firmicutes to be a proportionally more dominant phylum (∼95%) in cloacal and cecal samples of broiler chickens in Northern Ireland.However, just like Andreani et al. [41], other phyla such as Proteobacteria, Tenericutes, Actinobacteria, and Bacteroidetes were detected in lower taxa numbers.We note that the diference between our fndings and those of other authors [18,40] could be due to the diferences in environment and agroclimatic conditions.It is noteworthy that while our study was on poultry raised in free-range conditions in diferent agroclimatic conditions, the study by Kumar et al. [18] and Yan et al. [40] investigated microbial communities in chicken under controlled conditions.Furthermore, their investigations were based on the general microbial profles in the caeca and ilea of chicken, while the present study specifcally considered the bacterial communities of pathogenic potential in the cloacal and oropharyngeal swabs of several poultry.
Our results also showed that Desulfovibrio, Gallibacterium, and Mycoplasma were the most dominant genera in the cloacal samples across the poultry species, with Escherichia, Klebsiella, Chlamydia, Bacteroides, and Avibacterium also being detected in some poultry species, albeit in lower proportions.In contrast, Lactobacillus, Lachnoclostridium, Clostridium, and Bacteroides were the dominant genera in the cecum, cloaca, and feces [14], while Enterobacteria, Lactobacilli, and Enterococci were found to dominate the small intestines of chickens in Malaysia [11].On the other hand, Lactobacillus and Bacteroides were predominant in the small intestines of chickens in China [39].Another study by Schreuder et al. [44] found that Romboutsia, Gallibacterium, and Fusobacterium were most abundant across all samples, which equally contradicted the fndings of this study.Most of the bacteria detected in the current study are potentially pathogenic.In the oropharyngeal swabs, the most dominant genera were Chlamydia, Escherichia, Avibacterium, Gallibacterium, Mycoplasma, Klebsiella, and Neisseria, which are common etiological agents of poultry diseases.
At the species level, the hierarchical cluster maps revealed that Escherichia coli and Chlamydia ibidis were the most dominant bacterial species in the cloacal samples, while Streptococcus suis, Chlamydia ibidis, Gallibacterium anatis, Avibacterium paragallinarum, Mycoplasma gallinaceum, and Weissella confusa were detected in higher abundance in the oropharyngeal swab samples.Avian pathogenic Escherichia coli (APEC) causes colibacillosis, which is a severe respiratory and systemic disease in chickens [45], while Chlamydia infection in birds typically results in respiratory, ocular, and enteric symptoms, sometimes with a fatal outcome, although asymptomatic, latent infections are also common [46].Streptococcus species are considered a part of the normal fora in poultry, with infections resulting from Streptococcus occurring secondary to other primary infections.Tese infections can be acute or subacute/chronic forms due to septicemia, although they can be successfully treated.However, it is a zoonotic agent that causes severe disease in humans and is a major pig pathogen worldwide [47].Te role of Gallibacterium anatis and Avibacterium paragallinarum as etiologic agents has also been reported [48,49].Weissella confusa, on the other hand, has been proposed as a good candidate for the development of novel direct-fed microbial products [50].
It should be noted that the comparison of OTUs and taxonomic composition between the current study and other reported studies may be afected by approaches adopted in conducting the study [11].Other factors such as environment, treatment, feed additives, antibiotics, age, horizontal gene transfer, hygiene level, diet, poultry species, and agroclimatic considerations may also afect the poultry gut microbiome composition [11].
PCoA and NMDS plots showed no clear demarcation between bacterial communities from the cloaca and oropharynx across the poultry species under study.Our fndings are consistent with observations made by Kang et al. [14] who observed that samples from the cecum clustered with those from the cloaca in microbial structure.
Te KEGG and COG pathway analyses showed that cellular processes, nucleic acid metabolism, and environmental information processing were abundant in cloacal and oropharyngeal samples.Tese fndings are corroborated by the observations made by [18], who reported that metabolism, genetic information processing, cellular processes, International Journal of Microbiology human diseases, and organismal systems were the dominant functions predicted at level one in the KEGG pathway analysis.
Previously, eforts to identify and characterize antimicrobial resistance involved cloning of cultured bacteria, resulting in signifcant losses of several potential ARGs because most bacteria are not cultivable [18].Te increasing interest in AMR research is necessitated largely by concerns about the improper use of antibiotics in many settings globally, causing uncontrolled propagation of ARGs [18].Te continued use of antibiotics in livestock and humans equally propagates the spread of ARGs, becoming a major global health issue [18].Te Antimicrobial Resistance Genes Database (ARDB) was used to identify ARGs in the cloacal and oropharyngeal samples across all poultry species.Several genes responsible for antimicrobial resistance were detected in cloacal samples, with the most predominant genes conferring resistance to beta-lactamases (TEM116, TEM33, TEM4, TEM3, and aadA12).Other genes detected were those conferring resistance to tetracycline (tetC and tetW), aminoglycosides (APH3Ib), sulfonamides (sul2), and multidrug efux pumps (acrB and tolC).In addition, other proteins associated with AMR such as HNS and robA were also identifed.Ducks, guinea fowls, geese, and turkeys had the highest concentration of ARGs, underscoring the importance of these poultry as disseminators of AMR.Of major concern is that a combination of these ARGs is expected to confer signifcantly high resistance to a wide range of antibiotics, including beta-lactams, aminoglycosides, and tetracyclines, considering that these drug classes are the mainstream antibiotics that are indicated for the prophylaxis and treatment of bacterial infections in humans and animals [51].
Te major ARGs found in oropharyngeal samples confer resistance to β-lactamases (TEM16, TEM33, and TEM4), aminoglycosides (aadA12, aadA, and aadA15), and tetracycline (tetC and AAC6Ib7).Tis is contrary to the fndings of a study investigating AMR in Ethiopian backyard chickens which reported that the most predominant ARGs detected were tetracycline-resistant genes like tetQ, tetW, and tetX [15].Guinea fowls, pigeons, and geese had a higher concentration of ARGs in the oropharyngeal swab samples compared to the other poultry species.Cloacal samples generally had a higher number of ARGs compared to oropharyngeal swab samples, indicating that most of the microorganisms disseminating AMR in poultry species are enteric in nature.Our fndings underscore the need to understand bacterial pathogens afecting poultry and also fnd ways to control the inappropriate use of antimicrobials since ARGs can be transmitted from poultry to humans by consuming contaminated poultry products.
As has previously been adduced by Panyako et al. [52], the study's limitation is that the data generated come from pooled samples rather than from individuals.Tis has the potential to reduce the epidemiological strength of the study as it afects the study's potential to evaluate specifc diferences within individual samples.However, this approach provides an opportunity to access the diverse metagenomes that are present in the feces and oral secretions of these populations.

Conclusion
Our study investigated poultry's cloacal and oropharyngeal bacterial pathogens from diferent geographical locations in Kenya.Te results indicate the presence of many pathogenic bacteria in cloacal and oropharyngeal samples in the diferent poultry species studied, especially those belonging to the phyla Proteobacteria, Chlamydiae, and Firmicutes.In addition, using the KEGG and COG databases, some pathways related to metabolism, genetic information, and cellular processing were detected.We also report the abundance of ARGs that confer resistance to β-lactamases, aminoglycosides, and tetracycline in most of the poultry analyzed, raising concern about the dangers associated with continuous and inappropriate use of these antimicrobials in poultry production.Te ARG data generated in this study provides a valuable indicator of the use of antimicrobials in poultry by smallholder backyard farmers in Kenya.In addition, it is noteworthy that although this study was conducted earlier (between 2016 and 2018), the poultry farming practices in Kenya have not changed much since then.Terefore, the information generated is still informative for managing bacterial diseases, especially in backyard poultry raised under scavenging conditions.We recommend further work that compares metagenomes of poultry raised in both free range and controlled conditions to help assess the impact of the free-range environments on microbial communities of poultry.

Figure 1 :
Figure 1: Map of Kenya showing the main sampling sites with varying geographic and climatic conditions for cloacal and oropharyngeal swab samples (source: GeoCurrents map).

Figure 2 :
Figure2: Rarefaction curves of samples clustered at 90% sequence identity.Te rarefaction curves for each sample were plotted without replacement.Rarefaction is used to simulate an even number of reads per sample.In this study, the rarefaction depth chosen is 90% of the minimum sample depth in the dataset.For each poultry species and sample type, CN � chicken, DK � duck, GF � guinea fowl, GS � goose, PN � pigeon, and TY � turkey.

Figure 3 :
Figure 3: Alpha diversity measures (a) the number of OTUs and the Shannon entropy grouping of the diferent species by sample type; (b) alpha diversity measures by sample type; and (c) alpha diversity measures by species.

Figure 4 :Figure 5 :
Figure 4: Bacterial composition at phylum and genus levels; (a) cloacal relative abundance at phylum level; (b) oropharyngeal relative abundance at phylum level; (c) cloacal relative abundance at the genus level; and (d) oropharyngeal relative abundance at genus level for the diferent poultry species in all samples.A stacked column chart with taxonomic relative abundances (y-axis) by sample (x-axis).Te height of each bar chart relates to the taxonomic relative abundances in a sample.For each poultry species and sample type, CN � chicken, DK � duck, GF � guinea fowl, GS � goose, PN � pigeon, and TY � turkey.

Figure 5 :
Figure 5: Taxonomic abundances heat map based on log-transformed relative abundance values; (a) heatmap representation of cloacal taxonomy abundance of the dominant genera; (b) heatmap representation of oropharyngeal taxonomy abundance of the dominant genera; (c) heatmap representation of cloacal taxonomy abundance of the detected species; and (d) heatmap representation of oropharyngeal taxonomy abundance of the detected species (y-axis) in all samples (x-axis).Color scale from red (high abundance) to blue (low abundance) represents log-transformed relative abundance.For each poultry species and sample type, CN � chicken, DK � duck, GF � guinea fowl, GS � goose, PN � pigeon, and TY � turkey.

Figure 6 :
Figure 6: Comparison of poultry bacterial pathogens in cloacal and oropharyngeal swabs; (a) NMDS plot with Jaccard distance and (b) PCoA plot based on unweighted UniFrac distance matrices.

Figure 7 :
Figure 7: Comparison of poultry bacterial pathogens across poultry species using the PCoA plot based on the ordination of the distance matrix generated using Bray-Curtis distance.

Figure 8 :Figure 9 :Figure 10 :
Figure 8: Heatmap based on log-transformed relative abundance values showing the diferent abundances of predicted functions; (a) sample-wise KEGG pathway distribution plot and (b) COG pathway at diferent taxonomic levels between the two types of microbiomes across poultry species.Color scale from red (high abundance) to white (low abundance) represents log-transformed relative abundance.For each poultry species and sample type, CN � chicken, DK � duck, GF � guinea fowl, GS � goose, PN � pigeon, and TY � turkey.

Table 1 :
Number of raw reads, clean reads, assembled contigs, and observed number of OTUs identifed in cloacal swab samples.

Table 2 :
Number of raw reads, clean reads, assembled contigs, and observed number of OTUs identifed in oropharyngeal swab samples.

Table 5 :
Comparison of diferences in diversity between bacterial assemblages from the cloaca and oropharynx.

Table 6 :
Permutational analysis of variance testing whether the poultry species difer signifcantly.